Your browser doesn't support javascript.
Patterns and predictors of sick leave after Covid-19 and long Covid in a national Swedish cohort.
Westerlind, Emma; Palstam, Annie; Sunnerhagen, Katharina S; Persson, Hanna C.
  • Westerlind E; Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, and Sahlgrenska University Hospital, Per Dubbsgatan 14, 3 tr, 413 45, Gothenburg, Sweden.
  • Palstam A; Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, and Sahlgrenska University Hospital, Per Dubbsgatan 14, 3 tr, 413 45, Gothenburg, Sweden.
  • Sunnerhagen KS; Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, and Sahlgrenska University Hospital, Per Dubbsgatan 14, 3 tr, 413 45, Gothenburg, Sweden.
  • Persson HC; Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, and Sahlgrenska University Hospital, Per Dubbsgatan 14, 3 tr, 413 45, Gothenburg, Sweden. hanna.persson@neuro.gu.se.
BMC Public Health ; 21(1): 1023, 2021 05 31.
Article in English | MEDLINE | ID: covidwho-1249552
ABSTRACT

BACKGROUND:

The impact of Covid-19 and its long-term consequences is not yet fully understood. Sick leave can be seen as an indicator of health in a working age population, and the present study aimed to investigate sick-leave patterns after Covid-19, and potential factors predicting longer sick leave in hospitalised and non-hospitalised people with Covid-19.

METHODS:

The present study is a comprehensive national registry-based study in Sweden with a 4-month follow-up. All people who started to receive sickness benefits for Covid-19 during March 1 to August 31, 2020, were included. Predictors of sick leave ≥1 month and long Covid (≥12 weeks) were analysed with logistic regression in the total population and in separate models depending on inpatient care due to Covid-19.

RESULTS:

A total of 11,955 people started sick leave for Covid-19 within the inclusion period. The median sick leave was 35 days, 13.3% were on sick leave for long Covid, and 9.0% remained on sick leave for the whole follow-up period. There were 2960 people who received inpatient care due to Covid-19, which was the strongest predictor of longer sick leave. Sick leave the year prior to Covid-19 and older age also predicted longer sick leave. No clear pattern of socioeconomic factors was noted.

CONCLUSIONS:

A substantial number of people are on sick leave due to Covid-19. Sick leave may be protracted, and sick leave for long Covid is quite common. The severity of Covid-19 (needing inpatient care), prior sick leave, and age all seem to predict the likelihood of longer sick leave. However, no socioeconomic factor could clearly predict longer sick leave, indicating the complexity of this condition. The group needing long sick leave after Covid-19 seems to be heterogeneous, indicating a knowledge gap.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Sick Leave / COVID-19 Type of study: Cohort study / Observational study / Prognostic study Topics: Long Covid Limits: Aged / Humans Country/Region as subject: Europa Language: English Journal: BMC Public Health Journal subject: Public Health Year: 2021 Document Type: Article Affiliation country: S12889-021-11013-2

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Sick Leave / COVID-19 Type of study: Cohort study / Observational study / Prognostic study Topics: Long Covid Limits: Aged / Humans Country/Region as subject: Europa Language: English Journal: BMC Public Health Journal subject: Public Health Year: 2021 Document Type: Article Affiliation country: S12889-021-11013-2